Bank Customer Churn


Problem Statement -

Our goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given, if any individual customer will leave the bank or stay (customer churn). Also, rank all the customers of the bank, based on their probability of leaving.

To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.

 
 

Result - 86% Accuracy achieved by our neural network.



Technologies, Languages, Tools & more

Topics - ** Compiled in Python 3.8

# Artificial Neural Networks

# Tensorflow, NumPy, Pandas

# Sigmoid functions

# Building, Training & Compiling ANN

# Evaluating model

Notebook/IDE - Google Colab Notebook/Jupyter Notebook.

** All linked code and dataset used, with comments can be found on Github. The Github links for the respective projects have been added **


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